In this work, we showed for the first time how we could solve several hundred instances of a challenging non-convex trajectory optimization in parallel and in real-time. Our work has the potential of fundamentally changing how trajectory optimization or MPC are viewed and applied in the context of navigation in dynamic and cluttered environments. Instead of the established norm of computing one locally optimal trajectory, our batch optimizer paves the path for trying out several hundred random initializations of the problem in parallel to explore solutions in different homotopies and escape the local minima trap. In particular, the smooth trajectory sampling of [1]} gelled particularly well with our optimizer. We showed how MPC based on our batch setting substantially improved the success rate and tracking performance in densely cluttered and dynamic environments compared to state-of-the-art CEM.
